Bayesian analysis of longitudinal ordered data with flexible random effects using McMC: application to diabetic macular Edema data |
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| Authors: | Marjan Mansourian Iraj Kazemi Farid Zayeri Masoud Soheilian |
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| Institution: | 1. Department of Biostatistics, School of Medical Sciences , Tarbiat Modares University , PO Box 14115-111, Tehran , Iran;2. Department of Statistics , University of Isfahan , Isfahan , Iran;3. Department of Biostatistics, Faculty of Paramedical Sciences , Shahid Beheshti University of Medical Sciences , Tehran , Iran;4. Ophthalmology Department and Ophthalmic Research Center , Labbafinejad Medical Center, Shaheed Beheshti Medical University , Tehran , Iran |
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| Abstract: | In the analysis of correlated ordered data, mixed-effect models are frequently used to control the subject heterogeneity effects. A common assumption in fitting these models is the normality of random effects. In many cases, this is unrealistic, making the estimation results unreliable. This paper considers several flexible models for random effects and investigates their properties in the model fitting. We adopt a proportional odds logistic regression model and incorporate the skewed version of the normal, Student's t and slash distributions for the effects. Stochastic representations for various flexible distributions are proposed afterwards based on the mixing strategy approach. This reduces the computational burden being performed by the McMC technique. Furthermore, this paper addresses the identifiability restrictions and suggests a procedure to handle this issue. We analyze a real data set taken from an ophthalmic clinical trial. Model selection is performed by suitable Bayesian model selection criteria. |
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| Keywords: | Bayesian inference Gibbs sampling hierarchical Bayes proportional odds model skew-symmetric distributions |
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